Layered Injustices: Mapping Everyday Discrimination in Nursing Education through an Intersectional Lens
Bibliographic record
Abstract
BackgroundDespite stated commitments to equity, nursing education environments remain sites of pervasive everyday discrimination, especially for students with intersecting marginalized identities.PurposeThis study aimed to examine patterns of everyday discrimination among undergraduate nursing students using an intersectional lens, with particular attention to how experiences vary across overlapping axes of social identity and structural vulnerability.MethodA cross-sectional survey of 260 undergraduate nursing students was conducted at a large Canadian university. Everyday discrimination was measured using the Everyday Discrimination Scale (EDS), alongside sociodemographic variables related to race, gender, disability, financial insecurity, and English language status. Data were analyzed using ANOVA and factorial interaction models, with QuantCrit principles informing variable construction, modeling, and interpretation.ResultsEveryday discrimination was commonly reported and significantly higher among students identifying as racialized, especially those born in Africa, financially insecure, or with a disability. Interaction effects revealed that students at the intersection of multiple marginalized identities, particularly women with disabilities or racialized students with financial insecurity-reported the highest levels of discrimination.ConclusionFindings reveal that discrimination is structurally patterned and intensifies at the intersections of race, class, gender, migration, and disability. Through our intersectional and QuantCrit lens, this study advances how inequities are reproduced in Canadian nursing programs and raises urgent questions about ethics, responsibility, and institutional accountability, particularly in relation to the recruitment and support of racialized, international, economically disadvantaged students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".